Table1.docx

<p>Routine monitoring of shellfish growing waters for bacteria indicative of human sewage pollution reveals little about the bacterial communities that co-occur with these indicators. This study investigated the bacterial community, potential pathogens, and fecal indicator bacteria in 40 water samples from a shellfish growing area in the Chesapeake Bay, USA. Bacterial community composition was quantified with deep sequencing of 16S rRNA gene amplicons, and absolute gene abundances were estimated with an internal standard (Thermus thermophilus genomes). Fecal coliforms were quantified by culture, and Vibrio vulnificus and V. parahaemolyticus with quantitative PCR. Fecal coliforms and V. vulnificus were detected in most samples, and a diverse assemblage of potential human pathogens were detected in all samples. These taxa followed two general patterns of abundance. Fecal coliforms and 16S rRNA genes for Enterobacteriaceae, Aeromonas, Arcobacter, Staphylococcus, and Bacteroides increased in abundance after a 1.3-inch rain event in May, and, for some taxa, after smaller rain events later in the season, suggesting that these are allochthonous organisms washed in from land. Clostridiaceae and Mycobacterium 16S rRNA gene abundances increased with day of the year and were not positively related to rainfall, suggesting that these are autochthonous organisms. Other groups followed both patterns, such as Legionella. Fecal coliform abundance did not correlate with most other taxa, but were extremely high following the large rainstorm in May when they co-occurred with a broad range of potential pathogen groups. V. vulnificus were absent during the large rainstorm, and did not correlate with 16S rRNA abundances of Vibrio spp. or most other taxa. These results highlight the complex nature of bacterial communities and the limited utility of using specific bacterial groups as indicators of pathogen presence.</p

Image_1_Forecasting Prorocentrum minimum blooms in the Chesapeake Bay using empirical habitat models.pdf

Aquaculturists, local beach managers, and other stakeholders require forecasts of harmful biotic events, so they can assess and respond to health threats when harmful algal blooms (HABs) are present. Based on this need, we are developing empirical habitat suitability models for a variety of Chesapeake Bay HABs to forecast their occurrence based on a set of physical-biogeochemical environmental conditions, and start with the dinoflagellate Prorocentrum minimum (also known as P. cordatum).To identify an optimal set of environmental variables to forecast P. minimum blooms, we first assumed a linear relationship between the environmental variables and the inverse of the logistic function used to forecast the likelihood of bloom presence, and repeated the method using more than 16,000 combinations of variables. By comparing goodness-of-fit, we found water temperature, salinity, pH, solar irradiance, and total organic nitrogen represented the most suitable set of variables. The resulting algorithm forecasted P. minimum blooms with an overall accuracy of 78%, though with a significant variability ~ 30-90% depending on region and season. To understand this variability and improve model performance, we incorporated nonlinear effects into the model by implementing a generalized additive model. Even without considering interactions between the five variables used to train the model, this yielded an increase in overall model accuracy (~ 81%) due to the model’s ability to refine the regions in which P. minimum blooms occurred. Including nonlinear interactions increased the overall model accuracy even further (~ 85%) by accounting for seasonality in the interaction between solar irradiance and water temperature. Our findings suggest that the influence of predictors of these blooms change in time and space, and that model complexity impacts the model performance and our interpretation of the driving factors causing P. minimum blooms. Apart from their forecasting potential, our results may be particularly useful when constructing explicit relationships between environmental conditions and P. minimum presence in mechanistic models.</p

Metadata for stations sampled in the ARP.

<p>Measurements taken in conjunction with the metatranscriptomes are listed here. Asterisks highlight where concentration of the variable was below limit of detection.</p

Transcriptomic versus biogeochemical data.

<p>Panel A: The correlation between diatom microscope counts and log RuBisCO Form ID transcripts counts. Panel B: The inverse relationship of carbonic anhydrase transcript abundance to DIC concentration. Panel C: The inverse relationship between polyphosphate kinase transcript abundance and phosphate concentration. Station 2 and 25 had little or no phosphate, due to the diatom bloom, however <i>ppk</i> was not upregulated.</p

Sample size-normalized gene counts for the 31 biogeochemically-relevant genes.

<p>Values are the average of the duplicate samples, per 10 million sequences. Bolded/underlined numbers highlight the highest expression for that gene.</p

Salinity map of the May/June 2010 Amazon River Plume cruise aboard the RV Knorr.

<p>Salinity (PSU) from the underway system along the ship track was augmented with National Oceanographic Data Center profiles in regions of low coverage then interpolated and contoured.</p

Ratios of transcript abundance at stations 10:2 (black bars) and 25:2 (white bars).

<p>Station 10 has very high levels of eukaryotic nitrate transporter as well as chitin synthase compared to station 2. Note log scale. Stations 2 and 25 perform similar functions in the ARP. Thus the plot of the ratio of Station 25: Station 2 has smaller values than the ratio of stations 10 and 2.</p